Most of AI examples you might be aware of today – from chess-playing computers to self-driving cars – are predicated on deep learning and natural language processing. By means of these technologies, computers can perform specific tasks through processing various amounts of data and recognizing patterns in it.
What Is Artificial Intelligence?
While you might not be able to say exactly what artificial intelligence is, you probably use it every day. From Alexa and Siri to robot vacuums to autopilot, artificial intelligence is everywhere.
The term “artificial intelligence” was created way back in the 1950s by Marvin Minsky and John McCarthy. These two are the fathers of artificial intelligence and were years ahead of anyone else. According to them, artificial intelligence (AI) is any job done by a machine that needed a human in the past. Simple, right?
While the idea of AI started as something that seems simple enough today, the past 70+ years have changed everything. Today, artificial intelligence is beyond what anyone could have ever imagined.
How Does AI Work?
AI works by combining large amounts of data with fast processing and intelligent algorithms. This empowers the software to learn automatically from data patterns.
In the 2020s, artificial intelligence (AI) means teaching computers to do complicated jobs. Creating a type of “thinking” that is adaptable and evolving is the key goal. This is done through algorithms.
Algorithms are a set of rules that tells a computer how to act. Like a serious student, AI learns through hard work. The more work it does, the more it learns. This ability to develop intelligence outside of exact rules is called machine learning.
While a computer learning how to work might not seem like a big deal anymore, it’s a serious accomplishment. One of the core features of AI is its ability to see patterns.
Programmers can train an algorithm to guess what comes next. The information given for learning is called training data. The predictions that AI makes are often used to collect test data. This is the data that programmers look back at to decide how accurate those predictions were.
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